IS Chapter 3 – Flashcards

stores data at a single location that is accessible from anywhere. searches can be fast because the search engine does not need to check multiple distributed locations to find responsive data better control of data quality better IT security

integrate data from multiple databases and data silos and organizes them for analysis, knowledge, discovery, and to support decision making

small scale data warehouses that support a single function or one department enterprise that cannot afford to invest in a data warehouse start with data marts

tools and techniques process data and do statistical analysis for insight and discovery to discover meaningful relationships in data, keep informed in real time, detect trends, and identify opportunities and risks

used to manage the additions, updates, and deletions of data as transactions occur, and to support data queries and reporting OLTP systems Integrate with data collection systems such as TPS and business applications Store data in an organized way Provide facilities for accessing and managing data

provide access to data using declarative language

simplifies data access by requiring that users only specify what data they want to access without defining how they will be achieved

declarative language Select From Where

Data filtering and profiling: process and store data; inspect data for errors, inconsistencies, redundancies, and incomplete info Data integrity and maintenance: correct, standardize, and verify consistency and integrity of data Data synchronization: integrate, match, or link data Data security: check and control data integrity over time Data access: provide authorized access to data in both planned and ad hoc ways

designed to manage transaction data which are volatile breaks down complex info into simpler data tables to strike a balance between transaction processing efficiency and query efficiency

data that changes

higher performance, was distribution of data enables scalability and fault tolerance greater flexibility simpler administration

systems can increase in size to handle data growth or the load of an increasing number of concurrent users

allow apps on computers and mobiles to access data from both local and remote databases use client/server architecture to process information requests

poor quality data, lacks integrity and cannot be trusted Incomplete, out of context, outdated, inaccurate, inaccessible, overwhelming Cost of poor quality data: Lost Business Time spent preventing errors Time spent correcting errors

30 days data loses value

data loses value after 90 days

capability to capture, process, format, and distribute data in near real time or faster

percentage of total sales in a market captured by brand, product, or company

a measure of the precent of a company's revenue left over paying for its variable costs such as wages and raw materials increasing margin means earning more per dollar of sales the higher the operating margin, the better

pull together data from disparate sources and databases across an entire enterprise data marts are a lower cost

Extract from designated databases Transformed by standardizing formats, cleaning the data, and integrating Loaded into a data warehouse ETL

real time data warehousing and analytics Provide interaction with customer to provide superior customer service Respond to business events in near real time Share up-to date data among merchants, vendors, customers, and associates

Marketing and Sales: keeps people informed of status of products and marketing program effectiveness and profitability Pricing and contracts: calculate cost accurately Forecasting: estimates customer demand for products and services Sales: calculates sale profitability and productivity, analyzes results Financial: reduce risk or bad debt expense

low cost sensors collect data in real time and types of physical things regulate temp and climate detect air particles for contamination machinery conditions/failures engine wear/maintenance

apache processing platform, places no conditions on the structure of data it can process

provides a reliable, fault tolerant software framework to write applications easily process vast amounts of data in parallel on large clusters of commodity hardware

breaks up huge data and distributes subset among multiple servers where they are partially processed

the entire function of applying technologies, algorithms, human expertise, and judgment

broad category involves interpreting words and concepts in context

Exploration: simple word counts, topics consolidation Preprocessing: prepare and standardizing data, grammar and spell checking Categorizing and Modeling: uses statistical models and business rules to categorize data

data at moment of value- extract crucial facts from enterprise data in real or near real time Use for decision making, operational management Unites data technology, analytics, and human knowledge to optimize decisions

which data to use information overload: major problem for executives and employees data quality

Clearly articulate business strategies Deconstruct the business strategies into a set of specific goals and objectives Identify the key performance indicator Prioritize the list of KPI's Create a plan to achieve goals and objectives based on priorities Estimate the costs needed to implement the BI plan Access and update priorities based on business results Business driven development approach: identify data to use in BI and source systems. Starts with a business strategy and work backward to identify data sources and the data that need to be acquired and analyzed

documentation of a business or event, action, decision, or transaction

consist of hardware and software that manage and archive electronic documents and image paper documents index and store them

Access and use the content contained in documents Cut labor costs by automating business processes Reduce time and effort to locate required info Improve content security, reducing the risk of intellectual property theft Minimizes the cost associated with printing, storing, and searching for content

Data Latency Ability to handle the volatility of data Query Response time Data consistency Query predictability

Designed and optimized to ensure that every transaction gets recorded and stored immediately volatile OLTP systems

designed and optimized for analysis and quick response to queries nonvolatile, more stable OLAP systems Subject oriented